Source: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

• This course focuses on hacking skill
• We use R as the programming language
• Learning by doing is the best approach
• R is simpler than C/C++, Java, and Python
• Besides R, we will also cover basic data analysis

Source: http://www.kdnuggets.com/2015/05/r-vs-python-data-science.html

• R is mainly used when the data analysis task requires standalone computing or analysis on individual servers
• It’s great for exploratory work, and it's handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly
• R can even be part of a big data solution.

• When getting started with R, a good first step is to install the amazing RStudio IDE
• dplyr, plyr and data.table to easily manipulate packages
• stringr to manipulate strings
• zoo to work with regular and irregular time series,
• ggvis, lattice, and ggplot2 to visualize data, and
• caret for machine learning

• You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database

• Being a fully fledged programming language, it’s a great tool to implement algorithms for production use

• Python has no clear “winning” IDE. We recommend you to have a look at Spyder, IPython Notebook and Rodeo to see which one best fits your needs
• NumPy /SciPy (scientific computing)
• pandas (data manipulation) to make Python usable for data analysis
• matplotlib to make graphics
• scikit-learn for machine learning

• Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence
• Machine learning explores the study and construction of algorithms that can learn from and make predictions on data
• Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field

Source: https://en.wikipedia.org/wiki/Machine_learning

“Big Data” refers to data that grows so large that it is difficult to capture, store, manage, share, analyze and visualize with the typical hardware environments and database software tools

Source : Solutions Big Data IBM, 2012

Decisions that previously were based on guesswork, or on painstakingly constructed models of reality, can now be made based on the data itself http://www.cra.org/ccc/docs/init/bigdatawhitepaper.pdf

Source : New York Times

R & RStudio

• R is a scripting language for statistical data manipulation and analysis
• It was inspired by, and is mostly compatible with, the statistical language S developed by AT&T
• R has become more popular than S/S-Plus, both because it’s free and because more people are contributing to it

• a public-domain implementation of the widely-regarded S statistical language; R/S is the de facto standard among professional statisticians
• comparable, and often superior, in power to commercial products in most senses
• available for Windows, Macs, Linux
• in addition to enabling statistical operations, it’s a general programming language, so that you can automate your analyses and create new functions
• object-oriented and functional programming structure
• open-software nature means it’s easy to get help from the user community, and lots of new functions get contributed by users, many of which are prominent statisticians

R has many functional programming features. Roughly speaking, these allow one to apply the same function to all elements of a vector, or all rows or columns of a matrix or data frame, in a single operation. The advantages are important:

• Clearer, more compact code
• Potentially much faster execution speed
• Less debugging (since you write less code)
• Easier transition to parallel programming

R has provided a basic plotting envirorment for you.

But, ggplot2 is a more powerful visualization package in R.

You can download R from https://cran.r-project.org/ according to your OS.

You can download RStudio from https://www.rstudio.com/products/rstudio/download2/.

You can customize RStudio according to your own requirements. For example, you can change the code appearance, panel layout, and background color.

R Markdown provides an authoring framework for data science. You can use a single R Markdown file to both

• save and execute code
• generate high quality reports that can be shared with an audience

R Markdown documents are fully reproducible and support dozens of static and dynamic output formats.

• Install R & RStudio
• Customize your own RStudio environment and try to be familiar with it
• Start coding